The goal of this grant application is development of methods for understanding the consequences of antiretroviral treatment for the development of antiviral resistance. The development of resistance results from the acquisition of mutations over time-a process that both reflects and contributes to amount of replicating virus and perhaps also the fitness of virus. We intend to model these process jointly to study the complex co-evolution of viral genotype and such longitudinal measures as viral load or fitness, and CD4 count. Specifically our aims are: 1) Inclusion of time-varying covariates in hidden Markov models of viral genetic progression to examine the impact of viral diversity, viral load, and other factors on pathways to antiviral resistance. 2) Joint modeling of genetic pathways to resistance (using hidden Markov models) and time to virological failure, in settings where the genetic state is know with error. 3) Semiparametric methods for relating viral genotype to phenotype and to investigate the impact of specific mutations in the presence of others. 4) U-statistic approaches for two group comparisons of repeated measures of viral load, when times of measurement are arbitrary. 5) Assessing surrogacy of high-dimensional longitudinal markers subject to measurement error and missingness. Aim 1) investigates how factors like viral load, treatment, adherence history or other time-varying factors influence the pathways to resistance taken by the virus. Aim 2) investigates the influence of genetic pathway on the time course of longitudinal measures, like viral load or fitness. Aim 3) extends the methods developed in the last grant period to relate genotype to any response variable. The new methods are semi parametric; they should be more robust to nonnormality of errors and more powerful than the previous methods. Aim 4) arose from consideration of the problems raised in an AIEDRP concept sheet to investigate the impact of primary resistance mutations on the virological response to treatment. Aim 5) investigates the degree to which information on short-term response of viral load and on acquisition of resistance mutations can serve as surrogates for longer term clinical outcomes. [unreadable] [unreadable]